Reduction of speckle noise from medical images using principal component analysis image fusion

Kumar, Indrajeet; Bhadauria, H.S.; Virmani, Jitendra; Rawat, Jiledar

doi:10.1109/iciinfs.2014.7036562

Cited by 9 publications

(2 citation statements)

References 29 publications

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“…The resultant PCA underwent a process similar to wavelet shrinkage whereby the subspace components corresponding to small eigenvalues below a certain threshold were omitted and the remaining components were used to reconstruct the denoised image. In www.ijacsa.thesai.org [31], the authors proposed a hybrid of several denoising approaches where PCA is used to combine the outputs of those approaches to obtain the denoised image.…”

Section: Literature Reviewmentioning

confidence: 99%

Principal Component Analysis Based Hybrid Speckle Noise Reduction Technique for Medical Ultrasound Imaging

Kadah¹,

Elnokrashy²,

Alsaggaf³

et al. 2022

IJACSA

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Ultrasound imaging is the safest and most widely used medical imaging technique available today. The main disadvantage of ultrasound imaging is the presence of speckle noise in its images that may obscure pathological changes in the body and makes diagnosis more challenging. Therefore, many techniques were proposed to reduce speckle and improve image quality. Unfortunately, variations of their performance with different scan parameters and due to their methodologies make it hard to choose which one to adopt in clinical practice. In this work, we consider the problem of combining the information from multiple speckle filters and propose the use of principal component analysis to find the optimal set of weights that would retain the most information and hence would better represent the data in the final image. The new technique is implemented to process ultrasound images collected from a research system and the outcomes are compared to the individual techniques and their average using quantitative image quality metrics. The proposed technique has potential for utilization in clinical settings to provide consistently better-quality combined images that may help improve diagnostic accuracy.

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Section: Literature Reviewmentioning

confidence: 99%

Principal Component Analysis Based Hybrid Speckle Noise Reduction Technique for Medical Ultrasound Imaging

Kadah¹,

Elnokrashy²,

Alsaggaf³

et al. 2022

IJACSA

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“…In studies about Gaussian and Speckle in CT images, MRI or Ultrasounds [6,7], the filtering techniques and algorithms proposed are applied to an already reconstructed image. Nevertheless, they could also be applied to the projection data.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of image filters for their integration with LSQR computerized tomography reconstruction method

2020

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In CT (computerized tomography) imaging reconstruction, the acquired sinograms are usually noisy, so artifacts will appear on the resulting images. Thus, it is necessary to find the adequate filters to combine with reconstruction methods that eliminate the greater amount of noise possible without altering in excess the information that the image contains. The present work is focused on the evaluation of several filtering techniques applied in the elimination of artifacts present in CT sinograms. In particular, we analyze the elimination of Gaussian and Speckle noise. The chosen filtering techniques have been studied using four functions designed to measure the quality of the filtered image and compare it with a reference image. In this way, we determine the ideal parameters to carry out the filtering process on the sinograms, prior to the process of reconstruction of the images. Moreover, we study their application on reconstructed noisy images when using noisy sinograms and finally we select the best filter to combine with an iterative reconstruction method in order to test if it improves the quality of the images. With this, we can determine the feasibility of using the selected filtering method for our CT reconstructions with projections reduction, concluding that the bilateral filter is the filter that behaves best with our images. We will test it when combined with our iterative reconstruction method, which consists on the Least Squares QR method in combination with a regularization technique and an acceleration step, showing how integrating this filter with our reconstruction method improves the quality of the CT images.

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The Performance of Active-Contour and Region Growing Methods Against Noises in the Segmentation of Computed-Tomography Scans

Mousavi

Shariaty

Orooji

et al. 2020

Springer Proceedings in Physics

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Reduction of speckle noise from medical images using principal component analysis image fusion

Cited by 9 publications

References 29 publications

Principal Component Analysis Based Hybrid Speckle Noise Reduction Technique for Medical Ultrasound Imaging

Principal Component Analysis Based Hybrid Speckle Noise Reduction Technique for Medical Ultrasound Imaging

Evaluation of image filters for their integration with LSQR computerized tomography reconstruction method

The Performance of Active-Contour and Region Growing Methods Against Noises in the Segmentation of Computed-Tomography Scans

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